BACKGROUND AND AIM: Mobile monitoring has recently made it possible to measure the long-term trends of less commonly measured pollutants. While many different monitoring approaches have been taken, few studies have looked at the importance of study design when the goal is application to epidemiologic cohort studies. We carried out a simulation study to better understand the role of short-term mobile monitoring design on the prediction of long-term air pollution exposure surfaces. Since air pollution concentrations include random and systematic variability, we hypothesized that mobile campaigns will benefit from balanced designs that randomly sample from all seasons of the year, days of the week and hours of the day. METHODS: We simulated various short-term sampling designs using oxides of nitrogen (NOx) monitoring data from California air quality system (AQS) sites. Designs studied included a year-around, Balanced Design and two more common designs from the literature: a Rush Hours and a Business Hours Design. We evaluated the resulting annual average exposure predictions against the observations from each design and against the measured true concentrations. RESULTS:We found that the Balanced Design consistently produced accurate annual averages, while the Rush Hours and Business Hours Designs generally resulted in more biased estimates and model predictions. The superior performance of the Balanced Design was evident when predictions were evaluated against true concentrations; importantly, this superior performance was less detectable when predictions were evaluated against the measurements from the same sampling campaign since these measurements were themselves biased. CONCLUSIONS:Balanced design campaigns are expected to produce generally unbiased, long-term averages. Differential exposure misclassification could result from unbalanced designs, which may result in misleading health effect estimates in epidemiologic investigations. Appropriate study design is crucial for mobile monitoring campaigns aiming to assess accurate long-term exposure in epidemiologic cohorts. KEYWORDS: air pollution, study design, exposure assessment, oxides of nitrogen, environmental epidemiology, long-term exposure